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About 10,000 papers for “ensemble learning”

Ensemble learning

Ian H. Witten, Eibe Frank, Mark A. Hall et al. · Elsevier eBooks · 2025

Cited by 0

Ensemble Learning

Jitendra Kumar, Ashutosh Kumar Singh, Anand Mohan et al. · 2021

Cited by 0

Ensemble Learning

Martin Sewell · 2011

This note presents a chronological review of the literature on ensemble learning which has accumulated over the past twenty years. The idea of ensemble learning is to employ multiple learners and combine their predictions. If we have a committee of M models with uncorrelated errors, simply by averaging them the average error of a model can be reduced by a factor of M. Unfortunately, the key assumption that the errors due to the individual models are uncorrelated is unrealistic; in practice, the errors are typically highly correlated, so the reduction in overall error is generally small. However, by making use of Cauchy’s inequality, it can be shown that the expected committee error will not exceed the expected error of the constituent models. In this article the literature in general is reviewed, with, where possible, an emphasis on both theory and practical advice, then a taxonomy is provided, and finally four ensemble methods are covered in greater detail: bagging, boosting (including AdaBoost), stacked generalization and the random subspace method. Ensemble Learning

Cited by 0

Ensemble Learning

Harri Lappalainen, James W. Miskin · 2000

Introduction When we say we are making a model of a system, we are setting up a tool which can be used to make inferences, predictions and decisions. Each model can be seen as a hypothesis, or explanation, which makes assertions about the quantities which are directly observable and which can only be inferred from their eect on observable quantities. In the Bayesian framework, knowledge is contained in the conditional probability distributions of the models. We can use Bayes' theorem to evaluate the conditional probability distributions for the unknown parameters, y, given the set of observed quantities, x, using p (y jx ) = p (x jy ) p (y) p (x) (1) The prior distribution p (y) contains our knowledge of the unknown variables before we make any observ

Cited by 0

Ensemble learning.

Junyi Xu (2601184), Li Yao (113027), Le Li (356426) · Figshare · 2015

Cited by 0Open access

Ensemble Learning

Harri Lappalainen, James W. Miskin · 2000

Introduction When we say we are making a model of a system, we are setting up a tool which can be used to make inferences, predictions and decisions. Each model can be seen as a hypothesis, or explanation, which makes assertions about the quantities which are directly observable and which can only be inferred from their effect on observable quantities. In the Bayesian framework, knowledge is contained in the conditional probability distributions of the models. We can use Bayes' theorem to evaluate the conditional probability distributions for the unknown parameters, y, given the set of observed quantities, x, using p (y jx ) = p (x jy ) p (y) p (x) (1) The prior distribution p (y) contains our knowledge of the unknown variables before we make any obser

Cited by 0

Ensemble learning

Zhiyuan Liu, Ziyuan Gu, Pan Liu · Elsevier eBooks · 2025

Cited by 0

Ensemble Learning

Lior Rokach · Series in machine perception and artificial intelligence · 2019

Cited by 76

Ensemble Learning

2023

Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.

Cited by 0

Ensemble Learning

Taeho Jo · 2023

This chapter is concerned with the ensemble learning as an alternative advanced learning to the deep learning. It refers to the learning type for solving the problems such as classification, regression, and clustering, more reliably by combining multiple machine learning algorithms with each other. The typical schemes of ensemble learning are the voting which is the process of deciding the final answer by considering ones of multiple machine learning algorithms, the expert gate which is the process of nominating one among the machine learning algorithms as an expert which is suitable for solving the given input, and the cascading which is the process of deciding whether the current answer is adopted or the input is transferred to the next machine learning algorithm. We need to consider partitioning the training set which is viewed as a matrix with the two axes: the horizontal partition which partitions the training set into subsets and the vertical partition which partitions the attribute set. This chapter is intended to describe the ensemble learning as an advanced type of advanced learning.

Cited by 2Open access

ENSEMBLE LEARNING

2019

This chapter explains the basic characteristics of ensemble learning methodologies, and aims to compare bagging and boosting approaches. One of primary goals of data mining is to predict an “unknown” value of a new sample from observed samples. The ensemble learning methodology consists also of two sequential phases: training phase and testing phase. The random subspace method is a method of ensemble learning, which is based on theory of stochastic discrimination. The chapter distinguishes between different implementations of combination schemes for different learners. Combination schemes include the following: global approach; local approach; and multistage combination. Bagging was the first effective method of ensemble learning and is one of the simplest methods. Boosting is the most widely used ensemble method and one of the most powerful learning ideas introduced in ensemble learning community. AdaBoost is the most popular boosting algorithm. AdaBoost combine “weak” learners into a highly accurate classifier to solve difficult highly nonlinear problems.

Cited by 1

Ensemble Learning

Oliver Krämer · Intelligent systems reference library · 2013

Cited by 0